Use of parallel deterministic dynamic programming and hierarchical adaptive genetic algorithm for reservoir operation optimization

Reservoi r operation optimization (ROO) is a complicated dynamical ly constrained nonlinear problem that is important in the context of reservoir system operation. In this study, parallel deterministic dynamic programming (PDDP) and a hierarchic al adaptive genetic algorithm (HAGA) are proposed to solve the problem, which involves many conflicting objectives and constraints. In the PDDP method, multi-threads are found to exhibit better speed-up than single threads and to perform well for up to four threads. In the HAGA, an adaptive dynamic parameter control mechanism is app lied to determine parameter settings, and an elite individual is preserved in the archive from the first hierarchy to the second hierarchy. Compared with other methods, the HAGA provides a better operational result with greater effectiveness and robustness becau se of the population diversity created by the archiv e operator . Comparison of the results of the HAGA and PDDP shows two contradictory objectives in the ROO problem-economy and reliability. The simulation results reveal that: compared with proposed PDDP, the proposed HAGA integrated with parallel model app ears to be better in terms of power generation benefit and computational efficiency. 2013 Elsevier Ltd. All rights reserved.